Processing access anomalies in a storage network

ABSTRACT

A processing system operates by: storing a data segment as a set of encoded data slices, wherein the set of encoded data slices are dispersed storage error encoded and stored in at least one storage unit of a storage network; receiving, from a requestor, an access request associated with the data segment; detecting an access anomaly associated with the access request, the access anomaly having one of a plurality of anomaly types; denying the access request in response to detecting the access anomaly; generating, based on the one of the plurality of anomaly types, an anomaly detection indicator identifying the requestor; and sending the anomaly detection indicator to other devices of the storage network.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 120 as a continuation of U.S. Utility application Ser. No.17/301,470, entitled “Resolving Detected Access Anomalies in a VastStorage Network”, filed Apr. 5, 2021, which is a continuation of U.S.Utility application Ser. No. 16/554,939, entitled “Selecting A Subset OfStorage Units In A Dispersed Storage Network”, filed Aug. 29, 2019,issued as U.S. Pat. No. 10,996,895 on May 4, 2021, which is acontinuation of U.S. Utility application Ser. No. 15/841,863, entitled“Selecting Storage Units In A Dispersed Storage Network”, filed Dec. 14,2017, issued as U.S. Pat. No. 10,437,515 on Oct. 8, 2019, which is acontinuation-in-part of U.S. Utility application Ser. No. 15/837,705,entitled “Adding Incremental Storage Resources In A Dispersed StorageNetwork”, filed Dec. 11, 2017, issued as U.S. Pat. No. 10,387,070 onAug. 20, 2019, which is a continuation-in-part of U.S. Utilityapplication Ser. No. 15/006,735, entitled “Modifying Storage Capacity OfA Set Of Storage Units”, filed Jan. 26, 2016, issued as U.S. Pat. No.10,079,887 on Sep. 18, 2018, which claims priority pursuant to 35 U.S.C.§ 119(e) to U.S. Provisional Application No. 62/140,861, entitled“Modifying Storage Capacity Of A Storage Unit Pool”, filed Mar. 31,2015, all of which are hereby incorporated herein by reference in theirentirety and made part of the present U.S. Utility patent applicationfor all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 10 is a logic diagram of an example of a method of selectingstorage units in accordance with the present invention;

FIG. 11 is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention; and

FIG. 12 is a flowchart illustrating an example of resolving a detectedaccess anomaly in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2 , or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a dispersed storage and task (DST) execution unit and aset of storage units may be interchangeably referred to as a set of DSTexecution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each managing unit 18 and the integrity processing unit 20 maybe separate computing devices, may be a common computing device, and/ormay be integrated into one or more of the computing devices 12-16 and/orinto one or more of the storage units 36. In various embodiments,computing devices 12-16 can include user devices and/or can be utilizedby a requesting entity generating access requests, which can includerequests to read or write data to storage units in the DSN.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8 . In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1 . Note that the IOdevice interface module 62 and/or the memory interface modules 66-76 maybe collectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm(IDA), Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5 ); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1 throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3 , the computing device alsocreates a slice name (SN) for each encoded data slice (EDS) in the setof encoded data slices. A typical format for a slice name 80 is shown inFIG. 6 . As shown, the slice name (SN) 80 includes a pillar number ofthe encoded data slice (e.g., one of 1-T), a data segment number (e.g.,one of 1-Y), a vault identifier (ID), a data object identifier (ID), andmay further include revision level information of the encoded dataslices. The slice name functions as, at least part of, a DSN address forthe encoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4 . In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG. 8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4 . The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes the computing device 16 of FIG. 1 ,the network 24 of FIG. 1 , and a DST execution (EX) unit pool 490. Thecomputing device 16 includes the DS client module 34 of FIG. 1 . The DSTexecution unit pool 490 includes a set of at least W number of DSTexecution units (e.g., DST execution units 1-W). Each DST execution unitcan include a memory 84, which can be implemented utilizing the memory54 of FIG. 2 or another memory device. Each DST execution may beimplemented utilizing the storage unit 36 of FIG. 1 .

The DSN functions to select storage units for storage of sets of encodeddata slices 494, where a data object is dispersed storage error encodedutilizing an information dispersal algorithm (IDA) to produce aplurality of sets of encoded data slices 494, where each set of encodeddata slices includes an IDA width number=W of encoded data slices, andwhere a write threshold number=S of encoded data slices of each set ofencoded data slices are stored and maintained in accordance with therebuilding function (e.g., from time to time, missing and/or corruptedencoded data slices are replaced with rebuilt encoded data slices tomaintain the write threshold number of encoded data slices for each setof encoded data slices).

In a write-all configuration where there are a width number of slices tobe written, and a width number of possible locations to write them,there may be no possible optimization for selecting the write location,as there is only one possibility. However, when the number of slices tobe written (S) is less than the number of possible storage locations(W), there are (W choose S) possible combinations for how to select aunique S locations out of the W possible, where the (W choose S)function is defined as (W!/((S!*(W−S)!)), where ! is the factorialoperator.

Write ranking can be used by a computing device to select the optimalsingle choice out of the (W choose S) possible patterns of issuingwrites. The selection process can be optimized according to historicalperformance, storage unit utilization, storage unit response times,storage utilization of storage units, and/or round-robin styleload-leveling, among other possibilities. Once the computing devicemakes the selection of which S storage units to issues write requeststo, it can submit the write requests to the selected S storage units.

In an example of operation of the selecting of the storage units, the DSclient module 34 obtains resource information 498 for at least some DSTexecution units of the DST execution unit pool 490. The resourceinformation 498 can include one or more of a storage unit access latencylevel, a storage unit processing capability level, a storage unitutilization level, an available storage capacity, a storage unitavailability level, and/or a storage unit reliability level. Theobtaining can include at least one of interpreting a query response,interpreting an error message, interpreting a test result, interpretinga historical log of performance, and/or receiving the resourceinformation. For example, the DS client module 34 receives, via thenetwork 24, the resource information 498 from the DST execution units1-W.

Having obtained the resource information 498, the DS client module 34can receive a store data request 492. The store data request 492 caninclude one or more of a data object for storage, a store data requestindicator, and/or a name that corresponds to the data object forstorage. Having received the store data request 492, the DS clientmodule 34 can identify W available DST execution units of the DSTexecution unit pool 490 as candidates for utilization of storing of atleast some encoded data slices of each set of encoded data slices. Theidentifying can be based on one or more of the resource information 498,an interpretation of an error message, and/or an interpretation of astatus query response. For example, the DS client module 34 selects allof DST execution units 1-W when each DST execution unit is associatedwith favorable resource information (e.g., performance greater than aminimal performance threshold level).

Having identified the available DST execution units, the DS clientmodule 34 identifies a plurality of combinations of selecting S numberof DST execution units of the W available DST execution units. Forexample, the DS client module 34 identifies the plurality ofcombinations utilizing the formula, combination=W chooseS=(W!/((S!*(W−S)!)). For each combination, the DS client module 34assigns a rating level based on the resource information. The assigningincludes interpreting the resource information associated with theselection of S DST execution units to produce the rating level. Forexample, a more favorable rating level is assigned when a more favorableperformance level is associated with the particular combination of DSTexecution units. As another example, a more favorable rating is assignedwhen a more favorable storage capacity level is associated with theparticular combination of DST execution units.

Having assigned the rating levels to each of the combinations, the DSclient module 34 can select one combination based on one or more of therating levels, a round-robin approach, a load leveling approach, adistributed agreement protocol function ranking, a predetermination,and/or a request. For example, the client module 34 selects a highestrating level. As another example, the DS client module 34 utilizes theround-robin approach when two or more combinations are associated with acommon highest rating level. In various embodiments, the DS clientmodule identifies a plurality of permutations of selecting the S numberof DST execution units of the W available DST execution units, andselects from the plurality of permutations in the same fashion ofselecting from the plurality of combinations.

Having selected the one combination, the DS client module 34 canfacilitate storage of the data object of the store data request as aplurality of sets of encoded data slices in DST execution unitsassociated with the selected combination, where S of the encoded dataslices for each set of encoded data slices are stored in the associatedDST execution units (e.g., S number) of the selected combination. Forexample, the DS client module 34 dispersed storage error encodes thedata object to produce the plurality of sets of encoded data slices,selects S encoded data slices from each set of encoded data slices, andissues, via the network 24, a write slice request 496 to eachcorresponding DST execution unit of the selected S storage units, wherethe write slice requests 496 includes the selected S encoded data slices494 from each set of encoded data slices.

In various embodiments, a processing system of a computing deviceincludes at least one processor and a memory that stores operationalinstructions, that when executed by the at least one processor cause theprocessing system to obtain resource information for a subset of storageunits of a storage unit pool. W available storage units of the storageunit pool are identified in response to receiving a store data request.W choose S combinations of selecting S number of storage units of the Wavailable storage units are identified. A plurality of rating levels iscalculated based on the resource information, where each of theplurality of rating levels are assigned to a corresponding combinationof the W choose S combinations. One combination of the W choose Scombinations is selected based on the plurality of rating levels.Storage of data of the store data request is facilitated utilizing the Snumber of storage units of the selected one combination.

In various embodiments, obtaining the resource information includesinterpreting a performance log. In various embodiments, identifying theW available storage units is based on the resource information. Invarious embodiments, calculating the plurality of rating levels is basedon access performance metrics and storage availability metrics of theresource information.

In various embodiments, selecting the one combination includesidentifying a highest rated combination of the W choose S combinations.In various embodiments, selecting the one combination includes selectingfrom a plurality of highest rated combinations utilizing one of a randomselection and/or a round-robin selection. In various embodiments,facilitating storage of the data includes dispersed storage errorencoding the data to produce at least one set of encoded data slices andstoring the at least one set of encoded data slices in the S number ofstorage units of the selected one combination.

FIG. 10 is a flowchart illustrating an example of selecting storageunits. In particular, a method is presented for use in association withone or more functions and features described in conjunction with FIGS.1-9 , for execution by a computing device that includes a processor orvia another processing system of a dispersed storage network thatincludes at least one processor and memory that stores instruction thatconfigure the processor or processors to perform the steps describedbelow.

The method includes step 510 where a processing system (e.g., of adistributed storage and task (DS) client module and/or a computingdevice) obtains resource information for least some storage units of astorage unit pool. The obtaining can include at least one ofinterpreting a query request, interpreting an error message,interpreting a test result, interpreting a performance log, and/orreceiving the resource information. When receiving a store data request,the method continues at step 512 where the processing system identifiesW available storage units of the storage unit pool. The identifying maybe based on one or more of the resource information, interpreting systemregistry information, and receiving identities of the W availablestorage units.

The method continues at step 514 where the processing system identifiesW choose S combinations of selecting S number of storage units of the Wavailable storage units. For example, the processing system calculates Wchoose S for each combination. For each combination, the methodcontinues at step 516 where the processing system calculate a pluralityof rating levels based on the resource information, where each of theplurality of rating levels are assigned to a corresponding combinationof the W choose S combinations. The assigning may be based on one ormore portions of the resource information (e.g., access performance,storage availability).

The method continues at step 518 where the processing system selects onecombination based on the rating levels. The selecting can include atleast one of identifying a highest rated combination or selecting from aplurality of highest rated combinations utilizing at least one of arandom selection, a round-robin selection, and/or a predetermination.The method continues at step 520 where the processing system facilitatesstorage of data of the store data request utilizing the storage units ofthe selected combination. For example, the processing system dispersedstorage error encodes the data to produce encoded data slices, andstores S encoded data slices for each set of encoded data slices at Sstorage units of the selected combination.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to obtain resource information for a subset of storageunits of a storage unit pool. W available storage units of the storageunit pool are identified in response to receiving a store data request.W choose S combinations of selecting S number of storage units of the Wavailable storage units are identified. A plurality of rating levels iscalculated based on the resource information, where each of theplurality of rating levels are assigned to a corresponding combinationof the W choose S combinations. One combination of the W choose Scombinations is selected based on the plurality of rating levels.Storage of data of the store data request is facilitated utilizing the Snumber of storage units of the selected one combination.

FIG. 11 is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the user device 14 of FIG.1 , at least two distributed storage and task (DST processing units 1-2,the network 24 of FIG. 1 , and a set of DST (EX) execution units 1-n.The DST processing units 1-2 may be implemented utilizing the DSTprocessing unit 16 of FIG. 1 . Each DST execution unit may beimplemented utilizing the DST execution unit 36 of FIG. 1 .

The DSN functions to resolve a detected access anomaly. The accessanomaly includes a data access pattern invoked by the user device 14 tothe set of DST execution units via one of the DST processing units 1-2that is different by at least a difference threshold level than ahistorical and typical data access pattern. Specific examples of theaccess anomaly includes one or more of attempting access from a locationthat has never historically been used to initiate access, such as from adifferent Internet service provider, from a different Internet protocoladdress, and from a different remote physical location; too many failedauthentication attempts in a row (e.g., a bad login password); an accesspattern that statistically deviates from a normal access pattern; and anaccess type that requires a second level authentication check due tosignificance of the access type (e.g., a change of credentials,extending of a user permission, and deleting a significant amount ofdata).

In an example of operation of the resolving of the detected accessanomaly, while monitoring typical access of the user device 14 via theDST processing unit 1 (e.g., processing data access message is 1 on afrequent basis), issuing slice access messages 1, via the network 24, tothe set of DST execution units 1-n, DST execution unit 3 detects accessby the user device 14 via the DST processing unit 2 (e.g., receiving adata access message 2) and issuing, via the network 24, slice accessmessages 2 to the DST execution unit 3, and determines that the accessvia the DST processing unit 2 deviates from typical access thusidentifying the detected anomaly.

Having detected the access anomaly, the DST execution unit 3 queues theslice access message 2. The queuing includes at least one of storing theslice access message 2 locally in the DST execution unit 3 and sendingan anomaly detection indicator to other DST execution units. Havingqueued the request, the DST execution unit 3 initiates a secondaryauthentication process with the user device 14. For example, the DSTexecution unit 3 exchanges secondary proxy authentication messages 2with the DST processing unit 2 and the DST processing unit 2 exchangessecondary dedication messages 2 with the user device 14. The DSTexecution unit 3 indicates favorable second level authentication whenreceiving favorable secondary proxy authentication messages 2. Forexample, the DST execution unit 3 indicates a favorable second levelauthentication when a good password and/or credential is received. Asanother example the DST execution unit 3 indicates the favorable secondlevel authentication when a question (e.g., encrypting a nonce etc.) isanswered correctly. Alternatively, the DST execution unit 3 indicatesthe favorable second-level authentication only when receiving athreshold number of favorable second-level authentication indicationsfrom other DST execution units based on the other DST execution unitsperforming a similar second-level authentication on the secondary proxyauthentication messages 2. When the secondary authentication processindicates that the user device 14 has been authenticated, the DSTexecution unit 3 de-queues the queued slice access message 2 forprocessing. Alternatively, each other DST execution unit also de-queuesthe queued slice access messages 2 for processing.

FIG. 12 is a flowchart illustrating an example of resolving a detectedaccess anomaly. The method includes step 610 where a processing module(e.g., of a storage unit of a set of storage units) receives an accessrequest from one or more requesters (e.g., user devices). The receivingincludes at least one of receiving the access request directly from arequester and receiving a proxied access request via a proxy agent(e.g., a DST processing unit) of the one or more requesters.

The method continues at step 612 where the processing module detects anaccess anomaly of the received access request. The detecting includes atleast one of detecting an unfavorable access pattern, detecting a newrequester, detecting a new requester location, detecting one or more offavorable access times, detecting an access type associated with anaccess anomaly, and receiving an anomaly detection indicator fromanother storage unit of the set of storage units.

The method continues at step 614 where the processing module queues theaccess request. The queuing includes at least one of storing the accessrequest in a local memory and associating the stored access request withone or more other storage access requests associated with the accessrequest (e.g., a write request and a commit request for the same encodeddata slice).

The method continues at step 616 where the processing module issues ananomaly detection indicator to other storage units. For example, theprocessing module generates the anomaly detection indicator to includeone or more of the access request, and anomaly type, and an identifierof the requester. The method continues at step 618 where the processingmodule initiates a secondary authentication process with at least someof the one or more requesters. For example, the processing module issuesa secondary authentication request to a requester (e.g., generate thesecondary authentication request and send the generated secondaryauthentication request to the requester and/or a proxy of therequester).

The method continues at step 620 where the processing module receivesone or more of a secondary authentication response and an anomalydetection status from at least one other storage unit. For example, theprocessing module receives the secondary authentication response fromthe requester and processes the received secondary authenticationresponse to determine validity (e.g., answering a question, utilizingkey pair is to authenticate a response, etc.).

When the secondary authentication response and the anomaly detectionstatus is favorable, the method continues at step 622 where theprocessing module processes one or more associated queued accessrequests. For example, the processing module extracts the one or moreassociated queued access requests and processes the request in asequence in accordance with a request type (e.g., write request, commitrequest, and finalize request of a three-phase storage process).

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing system”, “processingmodule”, “processing circuit”, “processor”, and/or “processing unit” maybe used interchangeably, and may be a single processing device or aplurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing system, processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing system, processing module, module,processing circuit, and/or processing unit. Such a memory device may bea read-only memory, random access memory, volatile memory, non-volatilememory, static memory, dynamic memory, flash memory, cache memory,and/or any device that stores digital information. Note that if theprocessing system, processing module, module, processing circuit, and/orprocessing unit includes more than one processing device, the processingdevices may be centrally located (e.g., directly coupled together via awired and/or wireless bus structure) or may be distributedly located(e.g., cloud computing via indirect coupling via a local area networkand/or a wide area network). Further note that if the processing system,processing module, module, processing circuit, and/or processing unitimplements one or more of its functions via a state machine, analogcircuitry, digital circuitry, and/or logic circuitry, the memory and/ormemory element storing the corresponding operational instructions may beembedded within, or external to, the circuitry comprising the statemachine, analog circuitry, digital circuitry, and/or logic circuitry.Still further note that, the memory element may store, and theprocessing system, processing module, module, processing circuit, and/orprocessing unit executes, hard coded and/or operational instructionscorresponding to at least some of the steps and/or functions illustratedin one or more of the Figures. Such a memory device or memory elementcan be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device thatincludes a processor, the method comprising: storing a data segment as aset of encoded data slices, wherein the set of encoded data slices aredispersed storage error encoded and stored in at least one storage unitof a storage network; receiving, from a requestor, an access requestassociated with the data segment; detecting an access anomaly associatedwith the access, the access anomaly request having an unfavorable accesspattern; denying the access request in response to detecting the accessanomaly; generating, based on the unfavorable access pattern, an anomalydetection indicator identifying the requestor; and sending the anomalydetection indicator to other devices of the storage network.
 2. Themethod of claim 1, wherein the method further comprises: initiating asecondary authentication process with the requestor; receiving asecondary authentication response from the requestor; and processing theaccess request when the secondary authentication response is favorable.3. The method of claim 1, wherein the method further comprises: storingthe access request in a local memory; and associating the access requestwith at least one other access request, wherein the at least one otheraccess request includes a request for the data segment.
 4. The method ofclaim 3, wherein processing the access request includes: extracting theat least one other access request associated with the access request;and processing the access request and the at least one other accessrequest in a sequence in accordance with a request type of the accessrequest.
 5. The method of claim 4, wherein the access request and the atleast one other access request include a write request, a commitrequest, and a finalize request of a three-phase storage process.
 6. Themethod of claim 1, wherein the access request is received via adispersed storage and task (DST) processing unit that received theaccess request from the requestor.
 7. The method of claim 1, whereindetecting the access anomaly includes detecting one of a plurality ofanomaly types.
 8. The method of claim 1, wherein detecting the accessanomaly includes receiving a second anomaly detection indicator fromanother storage unit.
 9. The method of claim 1, wherein the anomalydetection indicator includes the access request.
 10. The method of claim1, wherein another storage unit receives another access requestassociated with the requestor corresponding to the access request andqueues the another access request in response to receiving the anomalydetection indicator.
 11. A processing system of a storage networkcomprises: at least one processor; a memory that stores operationalinstructions that, when executed by the at least one processor, causethe processing system to perform operations that include: storing a datasegment as a set of encoded data slices, wherein the set of encoded dataslices are dispersed storage error encoded and stored in at least onestorage unit of a storage network; receiving, from a requestor, anaccess request associated with the data segment; detecting an accessanomaly associated with the access, the access anomaly request having anunfavorable access pattern; denying the access request in response todetecting the access anomaly; generating, based on the unfavorableaccess pattern , an anomaly detection indicator identifying therequestor; and sending the anomaly detection indicator to other devicesof the storage network.
 12. The processing system of claim 11, whereinthe operations further include: initiating a secondary authenticationprocess with the requestor; receiving a secondary authenticationresponse from the requestor; and processing the access request when thesecondary authentication response is favorable.
 13. The processingsystem of claim 11, wherein the operations further include: storing theaccess request in a local memory; and associating the access requestwith at least one other access request, wherein the at least one otheraccess request includes a request for the data segment.
 14. Theprocessing system of claim 13, wherein processing the access requestincludes: extracting the at least one other access request associatedwith the access request; and processing the access request and the atleast one other access request in a sequence in accordance with arequest type of the access request.
 15. The processing system of claim14, wherein the access request and the at least one other access requestinclude a write request, a commit request, and a finalize request of athree-phase storage process.
 16. The processing system of claim 11,wherein the access request is received via a dispersed storage and task(DST) processing unit that received the access request from therequestor.
 17. The processing system of claim 11, wherein detecting theaccess anomaly includes detecting one of a plurality of anomaly types.18. The processing system of claim 11, wherein detecting the accessanomaly includes receiving a second anomaly detection indicator fromanother storage unit.
 19. The processing system of claim 11, wherein theanomaly detection indicator includes the access request.
 20. Theprocessing system of claim 11, wherein another storage unit receivesanother access request associated with the requestor corresponding tothe access request and queues the another access request in response toreceiving the anomaly detection indicator.